Method and system for use in monitoring properties of patterned structures

ABSTRACT

A method and system are presented for use in characterizing properties of an article having a structure comprising a multiplicity of sites comprising different periodic patterns, where method includes providing a theoretical model of prediction indicative of optical properties of different stacks defined by geometrical and material parameters of corresponding sites, said sites being common in at least one of geometrical parameter and material parameter; performing optical measurements on at least two different stacks of the article and generating optical measured data indicative of the geometrical parameters and material composition parameters for each of the measured stacks; processing the optical measured data, said processing comprising simultaneously fitting said optical measured data for the multiple measured stacks with said theoretical model and extracting said at least one common parameter, thereby enabling to characterize the properties of the multi-layer structure within the single article.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/596,670, filed Oct. 20, 2009, which is a U.S. National PhaseApplication under 35 U.S.C. 371 of PCT International Application No.PCT/IL2008/000966 which has an international filing date of Jul. 13,2008, and which claims priority from U.S. Provisional Patent ApplicationNo. 60/949,034, filed Jul. 11, 2007, all of which disclosures are herebyincorporated by reference.

FIELD OF THE INVENTION

This invention is generally in the field of semiconductor industry andrelates to a method and system for inspecting a patterned article(semiconductor wafer).

BACKGROUND OF THE INVENTION

There has been a long-standing need in the semiconductor industry tocharacterize the properties of a semiconductor device. As dimensions ofdevices in this industry are diminishing, increasingly sensitivemetrology tools and analysis techniques are required for measurement ofthe properties of these devices, in particular, devices comprising astack of thin films on a semiconductor substrate.

Optical metrology tools used for such measurements are typicallyellipsometry and reflectometry based tools. Reflectometry based toolstypically measure changes in the magnitude of radiationreflected/transmitted from/through the sample, and ellipsometry basedtools typically measure changes of the polarization state of radiationafter interacting with the sample.

Measured optical data, indicative of the detected radiation (reflectedand/or transmitted), can be analyzed to derive information regarding theoptical constants of materials included in the sample, as well as thelayer parameters, such as thickness and geometrical parameters ofpatterns (including critical dimension (CD), line spacing, line width,wall depth, and wall profile).

Examples of the measurement techniques of the kind specified aredisclosed in “Simultaneous Measurement of Six Layers in a Silicon onInsulator Film Stack Using Spectrophotometry and Beam profileReflectometry”, J. M. Leng at al., J. Appl. Physics, 81(8), 15 Apr.1997, pp. 3570-3578; US 2006/0167651; U.S. Pat. No. 7,259,850; U.S. Pat.No. 5,999,267; and U.S. Pat. No. 6,091,485.

Also known are methods based on selective material removal from the fullmulti-layer structure, which are disclosed in U.S. Pat. No. 7,289,234and U.S. Pat. No. 7,019,850 both assigned to the assignee of the presentapplication.

GENERAL DESCRIPTION

The need for characterizing the properties, and particularly opticalmaterial properties (such as n, k being the real and imaginary parts ofthe complex index of refraction) of thin films, is required for variousapplications. The latter includes but is not limited to enablement ofproper designs of patterned structures (such as wafers) suitable for arequired performance in an electronic device and control of suchproperties during the structure production; and creation of data to be apre-requisite for other optical measurements, e.g. scatterometry, whichare sensitive to optical material properties.

However, while characterization of a single uniform film is ratherstraightforward, the task of characterizing material properties, withhigh accuracy, in a structure/stack that contains several differentmaterials, is much more complex. Generally, obtaining sufficientlyaccurate measurements of these properties may dictate high spectralaccuracy across a spectral range for each of the material layers.Utilizing a single measurement of a thin layer stack may provide aspectral measurement of the required accuracy indicative of the measuredproperties of a single unknown layer, however the measured optical datais usually insufficient for accurate determination of the materialproperties of each of the materials comprising the full stack with therequired confidence level. It is particularly significant when thespectral responses of several of the stack layers comprising materialsare highly coupled, as is the case for example when utilizing both BARC(bottom antireflective coat) and photoresist layers.

The common practice currently used in the industry in order to obtainsufficiently reliable material properties is comprised of measurementstaken on several wafers using the “additive stack” methodology andanalytical modeling of the material properties. By utilizing ananalytical model for the material properties, a correlation betweendifferent wavelengths is obtained, thereby reducing the number ofindependent variables characterizing each material, and the correlationsbetween these parameters can be significantly reduced. The “additivestack” methodology typically utilizes several short-loop blanket waferswhich are created such that the first wafer contains only the substrateand a first film made of a first material, a second wafer contains thesubstrate and both first and second films of first and second materialsrespectively, etc. The total number of blanket wafers is dependent onthe number of the unknown materials whose properties are to be measuredand analyzed. The first material is typically analyzed based on themeasurements of the first wafer only. The results are then used in orderto reduce the number of unknown parameters on the second wafer fromwhich only the material properties of the second material are extracted,and so on. This method frequently suffers from inaccuracy, since errorsare carried over from one wafer to the next.

Utilizing the “additive stack” approach for diagnosing complexstructures presents additional limitations associated with thefollowing. In many cases, some of the material properties change duringsubsequent steps of the wafer's fabrication process. Consequently,characterization (by measurements) of the properties of a material asdeposited on the wafer (as is generally the case when utilizing blanketwafers) does not necessarily provide an exact description of thematerial properties evident in the final structure of the wafer (or at asubsequent step of the process) in which the effects of differentpatterning steps on the structures are present. Typically, the effectsof such subsequent steps (e.g. deposition of subsequent layers,patterning etc) on the material properties are neither measured norconsidered when utilizing the additive stack method. Although both theanalytical modeling and the additive stack based techniques mightprovide for obtaining material properties of complex structures, thesetechniques require the use of many specially designed wafers, lengthymeasurement/analysis processes, and are time consuming while involvinghighly skilled experts.

Thus, according to a first broad aspect of the invention, there isprovided a method for characterizing properties of an article having amulti-layer structure comprising a multiplicity of sites comprisingdifferent periodic patterns, the method comprising:

-   -   providing a theoretical model of prediction indicative of        optical properties of different stacks defined by geometrical        and material parameters of corresponding sites, said sites being        common in at least one of geometrical parameter and material        composition parameter;    -   performing optical measurements on at least two different stacks        of the article and generating optical measured data indicative        of the geometrical parameters and material parameters for each        of the measured stacks;    -   processing the optical measured data, said processing comprising        simultaneously fitting said optical measured data for the        multiple measured stacks with said theoretical model and        extracting said at least one common parameter, thereby enabling        to characterize the properties of the multi-layer structure        within the single article.

It should be noted that measurements in different stacks refers to acase of measurements in different sites (locations) and/or that ofmeasurements at the same site (location) but at different process'stages and thus characterized by different stacks at the same location.For simplicity however, in the description below, both of such optionswill be referred to as “sites” or “test sites” or “areas”, but it shouldbe understood that these expressions generally mean “different stacks”.

It should also be understood that at least two different stacks or sitesused in the measurements may include one patterned and one unpatternedsite (the so-called “solid site”); two different patterned sites; or twodifferent unpatterned (solid) sites. The technique of the presentinvention can be used for monitoring/controlling the article manufactureprocess using measurements taken at different steps in the process.Accordingly, a minimal measured set in this method would be a singlepatterned site measured in two process steps and thus having twodifferent patterns respectively.

It should be noted that optical properties of a stack/site undermeasurement are described by an optical response of the respective siteto interaction with an optical beam and may include diffraction,interference etc. effects. When speaking about a patterned site, thediffraction effects would be dominant. For simplicity, in thedescription below the general expressions “optical properties” and“interaction with optical beam” are referred to as being associated withdiffraction.

According to another broad aspect of the invention, there is provided ameasurement system for use in characterizing properties of an articlehaving a multi-layer structure comprising a plurality of differentperiodic patterns, the system comprising:

-   -   an optical measurement unit adapted for carrying out optical        measurements and generating optical measured data indicative of        geometrical parameters and material composition parameters for a        measured area on the article;    -   a control unit connectable to the measurement unit, the control        unit comprising:        -   a memory utility for storing reference data comprising a            theoretical model of prediction, said model being indicative            of optical properties of different stacks in a multi-layer            structure defined by geometrical and material composition            parameters of corresponding sites, where said sites are            common in at least one of geometrical parameter and material            composition parameter;        -   a processor utility configured and operable for processing            and analyzing the optical measured data, said processing and            analyzing comprising simultaneously fitting said optical            measured data for the multiple measured stacks with said            theoretical model and extracting said at least one common            parameter, thereby enabling to characterize the properties            of the multi-layer structure within the single article.

According to yet further aspect of the invention, there is provided asystem for use in characterizing properties of an article having amulti-layer structure comprising a plurality of different periodicpatterns, the system comprising a control unit adapted for receivingoptical measured data indicative of geometrical parameters and materialcomposition parameters of a measured area on the article and comprising:a memory utility for storing reference data comprising a theoreticalmodel of prediction, said model being indicative of optical propertiesof different stacks a multi-layer structure defined by geometrical andmaterial composition parameters of corresponding sites, where said sitesare common in at least one of geometrical parameters and/or at least oneof material composition parameters; and a processor utility configuredand operable for processing and analyzing the optical measured data,said processing and analyzing comprising simultaneously fitting saidoptical measured data for the multiple measured patterns with saidtheoretical model and extracting said at least one common parameter,thereby enabling to characterize the properties of the multi-layerstructure within the single article.

The present invention discloses a technique for parameterscharacterization in general and particularly optical material propertycharacterization of a wafer of multi-layer material stack. The methoddescribed herein utilizes optical measurements of a single wafercomprising multiple material layers and having a multiplicity ofdifferent stacks (sites), e.g. patterned areas of the wafer.

The present invention further utilizes a physical theoretical modeldefined for each of the structures that are measured and analyzed. Thesemodels are used to measure accurately the global parameters associatedwith the results of optical measurements. This is usually achieved usinginverse regression fit techniques adapted to correlate the actualresults of optical measurements of the sites with the prediction ofmeasurement results of the various sites as given by the site's modelsand to optimize the models parameters such that the predicted resultsfit the measured results. This procedure provides optimization of themeasured parameters to obtain accurate values of these parameters withhigh confidence level.

In some embodiments of the invention, use is made of a multiplicity ofdifferent stacks which are produced using most or all of the samematerials. Such stacks are typically located at different test sites andmay include the so-called “solid sites” being unpatterned sites/areasand different patterned sites areas including periodic patterns (in 2Dor 3D) with different pitch, features shape and/or duty cycle. The testsites can be made specially for measurement proposes; or alternativelyor additionally existing sites within the product region of the wafercan be used as test sites.

The present invention utilizes optimization algorithms (such as inverseregression fit algorithms) to characterize common (global) parameters ofthe test sites. Physical, theoretical models of the sites'characteristics are adopted and utilized for predicting the measurementsoptical response expected by each test site. These physical models aretypically based on physical theories such as Fresnel equations forcharacterizing the optical response of solid sites and RCWA (rigorouscoupled-wave analysis) initially developed by Moharam and Gaylord anddisclosed in M. G. Moharam and T. K. Gaylord, J. Opt. Soc. Am, 71, pp.811-818 (1981), or another Method for calculating diffraction fromgrating structures as disclosed in U.S. Pat. No. 6,657,736. The modelsare parameterized to enable fitting the model to the sites' parameters,such as line width or layer thickness and layers' material parameters.

Generally although global parameters, common for at least two sites aremeasured, local parameters not necessarily identical within the sitesare introduced to enable breaking the correlations between the globalparameters measured and to enable an accurate determination of theglobal parameters of interest.

It is not straightforward to obtain correct theoretical parametersfitted to the results of the measurements, particularly when the degreeof correlation between the required parameters is high or when thesensitivity of these parameters of interest is significantly below thesensitivity level of other parameters. In such cases regular, known inthe art methods, fail to distinguish the effect of these parameters, andthus poor confidence level of these parameters is typically achieved.

However, the inventors of the present invention have realized thatutilizing a multiplicity of structures made on several test sites, inwhich several parameters are common for some of the structures (i.e.global parameters) and other parameters (local) distinguish thesestructures from each other, may provide higher sensitivity values andreduce the correlations of the global parameters of interest. This ideais beneficial for analyzing and measuring the values of the commonparameters with higher confidence level and providing higher fittingvalues (typically measured by merit function) of the measured opticalresponses to the predictions of sites' characterizing models.

To this end, it should be noted that common, global parameters maycomprise any of the parameters characterizing the sites, including theparameters characterizing the material properties, layer thicknesses andgeometrical parameters (e.g. CD, duty cycle etc.) as long as theseparameters are common to at least some of the test sites inspected.

The present invention further provides an optimization method forcharacterization of single wafer characteristics. The method exploitsthe benefits of analyzing and measuring global parameters by utilizing amultiplicity of test sites as described above and provides a systematicapproach for optimization of multiple test sites measurements enablingan accurate fitting of the physical models of the test sites to themeasured results, thus providing higher confidence levels formeasurements of these parameters.

Further to the above, although any global parameter can be analyzedaccording to this method, the method is highly suited for measurementsof the optical material properties of the materials comprising thesample. Typically, when measuring solid wafer structures the sensitivityof the measurements to the material optical properties may be well belowthe sensitivity to other parameters, such as layers thickness.Furthermore, for some materials the optical properties are highlycorrelated (such as the case with BARC and photoresist materials). Forthese reasons, accurate measurement of these properties is difficultwith existing methods.

Generally, characterizing the material properties of a multi-layermaterial stack structures comprising several, different, materials posesseveral difficulties. Typically, the amount of data received by a singlemeasurement of such stack of materials may be insufficient forcharacterizing the optical properties of all the materials comprisingsaid stack. For example, a spectroscopic measurement of the complexrefraction index (n+ik) of a material stack comprising M differentmaterials with a required spectral resolution of N wavelengths acrossthe spectral range provides for N (if only n or k are measured) or 2N(if both n & k are measured) data points. Characterizing the complexrefraction index (n+ik) of all the different materials generallyrequires for 2*M*N independent data points and thus a single measurementprovides insufficient data for an accurate determination of theseproperties. The present invention solves this problem by determiningthese parameters utilizing multiple measurements of several sites inwhich these parameters are common and further provides an optimizationalgorithm adapted to distinguish and characterize these parametersthrough the measurements results. Further to the above, said method, forcharacterization of material properties, enables determination of thesaid properties as they are evident at the final stage of the waferfabrication. The method resolves many of the limitations of the standardmethodology of additive stack and model based analysis described above.

Since the structures (test sites) have at least some distinguishinglocal parameters different from other sites (e.g. different geometricalparameters), the measurements of these sites and the predictive physicalmodels provide different spectra and hence additional information isobtained that is not contained in the non-patterned areas. Since thesame materials are present in all structures, it is valid to combine theinformation taken from all these different sites into a singleoptimization problem and solve for the material properties acrossdifferent sites at the same time.

It is a further aspect of the current invention to combine measurementstaken in several steps of the process, e.g. both before and after etch,deposition or polish, allowing for a wider variety of structures andallowing more information to be collected. By measuring the same site inseveral steps of the process, a multiplicity of spectra is obtained,which can also reduce the number of different measurement sites requiredin order to achieve the required amount of information to extract thedesired global parameters. Hence another aspect of this invention is themeasurement of a single site on a single wafer over several steps of theprocess thereby providing a multiplicity of measured data allowing saidextraction of global parameters.

An additional aspect of the invention is the use of differentmeasurements taken from different locations on the wafer representingsomewhat different structures, e.g. due to center-to-edge variationswhich are typical of many processes (CMP, CVD).

It is a further aspect of this invention that use can be made ofdeliberate modifications done in one of the processes to increase theamount of information available for the optimization. For example, usecan be made of the degrees of freedom allowed by a photolithographyprocess that is one of the steps used for the production of the wafer.By exposing different fields on the wafer using different exposureand/or focus conditions, it is possible to create different gratingstructures in different areas of the same wafer, thereby significantlyincreasing the amount of information which can be collected from asingle wafer even to the degree that a single site per field issufficient.

It is a yet further aspect of this invention to provide a method forprocess control in which the material properties are periodicallymonitored during a production process using standard product wafers asopposed to the use of special wafers that require intervention of thestandard process flow. By measuring a multiplicity of sites on standardprocess wafers and submitting the results to the analysis methodsdescribed in this invention, it is possible to continuously monitor thematerial properties of various layers in the process. The measuredmaterial properties can then be utilized e.g. for flagging changesduring normal operation of process tools such as deposition chambers,for qualifying such process tools after periodic maintenance, etc. Sincethe simultaneous fitting of a multiplicity of sites is more, timeconsuming than a standard fitting process of a single measurement site,it is possible that the data for said multiple-site analysis is run as aseparate, parallel process on the same processing unit or is sent forprocessing on a separate, potentially remote processing unit, therebyallowing the measurement system to provide a periodic materialcharacterization analysis without delaying the continuous use of thesame measuring system for standard single-site measurements.

This method provides several solutions to the issues that limit thestandard method as described above. Since all structures are defined onthe final layer, there is no need for producing short-loop wafers tocreate an “additive stack”, thus saving cost and shortening thetime-to-solution. Moreover, since all structures are produced from thefull stack, all aspects of material changes after deposition areautomatically taken into account. Additionally, since the onlyrequirement is for the correct test sites (or within-product sites) tobe present on the mask, this method allows following up on the materialproperties during serial production in the exact same way as done duringthe initial recipe setup, allowing better process control, which isimpossible with the standard additive stack method. In order to furtherenhance the amount of available information, it is possible to measurethe different sites using multiple measurement methods, e.g. measurepolarized spectra or ellipsometric parameters at a multiplicity ofincidence angles.

Clearly, the use of this method relies on the ability to model thediffraction from complex structures at various illumination conditions,however this capability is basically equivalent to the modelingcapabilities required by scatterometry and are hence widely known. Thedifference is in implementing this modeling towards the characterizationof material properties together with the geometrical parameters, whereasin previous works material properties are assumed known and the targetis to optimize for geometrical parameters. The quality of the solutionis largely due to the ability to correctly combine a large body ofinformation without reaching the wrong solution, for example due tolocal minima of the fitting function.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 is a schematic illustration of an example of a system suitablefor carrying out a method of the present invention;

FIGS. 2A-2F are schematic diagrams showing a plurality of areas/sites ofa single article sharing most or all of the same material stack: FIG. 2Aillustrates a non-patterned (solid) area; FIGS. 2B-2D illustrate severalpatterned sites with various pitch and duty cycle values in which thepatterning is at the upper layer, e.g. typical of post developmentmeasurements in lithography; and FIGS. 2E-2F illustrate two patternedsites with various pitch and duty cycle values that may be measured atdifferent steps of the process, e.g. after etching;

FIG. 3 is a flow diagram exemplifying an optional stage of preliminarydesigning the test areas;

FIG. 4 is a flow diagram exemplifying an embodiment of the parameteroptimization technique of the present invention;

FIG. 5A shows a typical sensitivity analysis table of a patterned areawith CD=45 nm and duty cycle of 1:1 (equal line to space);

FIG. 5B shows a typical sensitivity analysis table of a patterned areawith CD=45 nm and duty cycle of 1:5 line to space;

FIG. 5C illustrates a typical structure under investigation in FIGS. 5Aand 5B;

FIG. 6A illustrates parameter correlation matrix of a solid(un-patterned) area that exemplifies high correlation between theparameters;

FIG. 6B illustrates parameter correlation matrix of a patterned(grating) area that exemplifies low correlation between thecharacterizing parameter;

FIGS. 7A and 7B exemplify spectra fitting during two successive steps ofthe model optimization procedure, respectively, for three typical sitesin lithography: Solid Photoresist layer on Barc layer on Siliconsubstrate, Solid Barc Layer on Silicon substrate, and grating ofPhotoresist on Barc layer on Silicon substrate; and

FIG. 8 shows a flow diagram of two parallel processes controlprocedures, according to an example of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Referring to FIG. 1, there is schematically illustrated an example of ameasurement system, generally designated 10, suitable to be used forimplementing the technique of the present invention for monitoring(measuring/inspecting) the properties of a wafer W. The latterconstitutes an article comprising a patterned structure. The article hasat least two different stacks (e.g. within different sites), at leastone of them having a periodic pattern; thus the article may havemultiple patterned sites/areas (generally, stacks) and possibly also oneor more unpatterned sites/areas (stacks).

The system 10 comprises a control unit 12 which is typically a computersystem including one or more computers, each including inter alia amemory utility 12A, a processor utility 12B, a data presentation utility(e.g. monitor) 12C. The control unit is configured and operable forreceiving and processing optical measured data MD, e.g. coming from anoptical measurement unit 14. The measured data is indicative of thegeometrical parameters and material composition parameters of one ormore measured sites/areas of an article under measurement. To this end,the control unit utilizes a previously provided theoretical model ofprediction. This model is configured to be indicative of diffractionproperties of at least some of non-patterned and patterned areas on thearticle similar to that under measurement. The diffraction propertiesare defined by geometrical and material-associated optical parameters ofthe measured areas. The model assumes that the measured areas havecommon geometrical parameter(s) and/or material-associated opticalparameter(s).

The theoretical model is previously appropriately selected and stored,e.g. in the memory utility of the control unit (e.g. being distributedbetween memories (databases) of different computers connectable to oneanother), or in a separate database accessible by the control unit via acommunication network. It should also be noted that the measured datamay be processed off-line, i.e. in a post-measurement session; oron-line (real time). The control unit 12 may be connectable to themeasurement unit 14 via wires or wireless signal transmission.

The optical measurement unit 14 may or may not be the constructionalpart of the system of the present invention. The system of the presentinvention (control unit 12) is however configured to be capable ofanalyzing measured data obtained by a predetermined type of opticalmeasurement. In other words, the theoretical model is selected todescribe diffraction and/or interference properties of an article(similar to that under measurement) and measured according to apredetermined technique. The optical measurement unit may be configuredfor carrying out spectral ellipsometry or spectral reflectometry,preferably operable in a zero order diffraction detection mode. Itshould be noted that in some embodiments, the optical measurement unitmight include measurement tools of different types, or in some otherembodiment the optical unit may utilize the same measurement tool butoperated with different measurement modes, to thereby carry outdifferent measurements on the same or different sites on the article.

The control unit 12 (its processor utility 12B) is preprogrammed forprocessing the optical measured data. This processing includessimultaneously fitting the optical measured data for the multiplemeasured areas with the theoretical model and extracting at least onecommon parameter. This enables to characterize the properties of themulti-layer structure within the single article/structure. This is theso-called “Single Wafer Parameters Optimization” (SWPO) technique. Sucha method may for example utilize a parallel regression fit algorithm foroptimizing certain properties, typically optical properties, of certainmaterials/layers.

The SPWO technique can be used for characterizing properties of anarticle having a patterned structure (e.g. wafer), i.e. a structurewhich has patterned areas and possibly also one or more non-patternedareas (solid stack areas), keeping in mind that such “areas” constitutestacks associated with the same or different locations on the article.The method utilizes processing and analyzing measured data from aplurality of areas or sites, which thus serve as test sites and arelocated within a test region of the article outside a product region ofsaid article or located within the product region. The areas can bedescribed by their geometrical and material composition parameters. Theareas typically differ from each other by some of the geometrical and/ormaterial properties (i.e. local properties/parameters), while some ofthese properties are common for at least some of the areas (i.e.common/global properties/parameters).

A theoretical (physical) model is used to provide predictions indicativeof the interference/diffraction properties of at least some of thedifferent areas. More specifically, the model has multiple sub-modelscorresponding to different areas, i.e. areas different in thegeometrical parameter(s) and/or material composition parameter(s). Asindicated above, such diffraction properties are generally dependent onthe area properties (e.g. geometrical and/or material parameterscharacterizing the area). Optical measurements are performed on selectedareas to provide measured data indicative of the diffraction propertiesfor each of these areas.

Then, simultaneous fitting is applied for multiple sub-models and theircorresponding measured data pieces, e.g. inverse regression fit methods.This is typically achieved by an iterative alteration of the parametersof the theoretical sub-model until an adequate level of fit is achievedsimultaneously for the measured areas as is further described below.

When an adequate level of fit is achieved for the sub-models and therespective measured data, e.g. defined by a predetermined value of thetotal merit function, the values of the common parameters in themeasured areas can be extracted to be further used to characterize theproperties of the article.

The technique of the present invention allows for characterizing theproperties of the article by measurements taken from the single article,i.e. eliminating a need for comparing data measured from differentsimilar articles. However, the invention is not limited tosingle-article measurements, and sometimes measured data to be processedis that collected from the measured areas located on two or morearticles (wafers). In some embodiments of the invention, commonparameters, common for at least some of the measured areas, can beoptimized.

The theoretical model suitable to be used in the present invention mayinclude for example a description of the spectral response of each siteon the wafer (as a function of several parameters, such as materialOptical parameters and geometrical or structural parameters (e.g.expressed by functional presentation of the complex refraction index ofcertain materials and its dependency on the wavelength, layerscomposition and thickness). Generally, a theoretical model of this typedoes not always accurately describe/predict the measurement results.This is due to some effects such as process variations that may changethe nominal state and interactions or other interface effects betweenlayers that are, usually, not fully included within the theoreticalmodel of the wafer.

To this end, multiple measurements of several, generally not similar,blanket wafers (e.g. Additive stack approach) can be used to enableoptimization of measurements by providing separate, independentmeasurements of certain parameters (e.g. by measuring a wafer containinga first layer, then a wafer containing first and second layers etc.) andto provide an optimized model better fitted to measurement results. SPWOhowever preferably utilizes measurements of a multiplicity of testsites/areas fabricated on a single wafer (or on several wafers havinggenerally the same material and structural properties). Generally, eachof these areas is made with different geometrical properties, forexample such areas may comprise several gratings with different CDs(e.g. line width) and duty cycles or periods as will be exemplifiedfurther below with reference to FIGS. 2A-2F). SPWO utilized thegeometrical parameters of each area to provide modeled prediction ofexpected measurement results (e.g. local sub-models).

In some cases, the test sites/areas and their geometrical parameters arespecially designed to enable extraction and optimization of certainparameters of interest of the theoretical model characterizing theeffect of the material optical properties and geometrical properties ofthe wafer on the measurement results. This optimization process can beperformed using data gathered directly through the measurements resultsor indirectly through further analysis of the theoretical models. Insome other cases, the measurements are carried out on several sitesalready fabricated on the desired wafer which are found suitable for theanalysis of the desired parameters/properties.

Thus, measurement results obtained by measuring several test sites (e.g.patterned areas) of the same wafer can be used together to analyze andoptimize common (“global”) parameters of the theoretical model (such asthe complex refraction index of certain layers/materials).

Different patterned sites formed on a wafer are usually differentiatedby certain geometrical parameters (e.g. duty-cycle, CD, periods, etc)and certain material optical parameters (e.g. refractive index andextinction coefficient). On the other hand, these patterned sites,fabricated on the same wafer, have in common at least one globalstructural parameter (e.g. the thickness of under layers) or commonoptical constants of materials. These global parameters, beingessentially the same in all sites measured on the same wafer, are usedfor modeling theoretically the measured properties (such asreflectivity) of different test sites.

The following is an example of the SWPO method of the present invention:

Optionally, the design/analysis and optimization of the geometricalparameters of the multiple areas is carried out. This procedure is aimedat appropriately selecting the areas/sites on the wafer to be used inmeasurements. This step can be done before designing a mask to be usedfor manufacturing the test sites. Alternatively, this step can be aimedat selecting some of the sites among a multiplicity of candidatemeasurement sites already existing on the mask/wafer. In both cases, theoptimization of the design or selection of the test sites is performedprior to actual measurements (i.e. is performed offline) based ontheoretical calculations and does not require actual wafers. In caseswhere this step is performed prior to the mask design, the theoreticalcalculation can be linked to DBM (Design Based Manufacturing) techniqueswhich identify the sites, verify their matching to design rules and OPC(Optical Proximity Correction) rules. This step will be morespecifically described below with reference to FIG. 3).

Thus, either after the above described procedure or without it, one ormore wafers are provided (produced) containing a combination of sites,including multiple patterned and optionally also non-patterned (solid)site(s). When a sufficient variety of sites exists on the same article(which is usually the case with semiconductor wafers) then the use of asingle wafer is sufficient for the purposes of the present invention.

Optical measurements are applied to some of the sites on the wafer usingoptical metrology tools. The type and variety of tools can be chosen ona case-to-case basis to measure various properties of the wafer withinthe measured sites, for example the reflectance spectra can be obtainedat various polarization states or ellipsometric parameters measurementscan be taken at a single or at multiple incidence angles. Measurementsmay also be taken at multiple steps of the process.

The so obtained measured data is processed for optimization of thematerial properties of at least some of the materials. The processing ofthe measured data is based on physical models and optimizationalgorithms as will be further described below.

The SWPO of the present invention can be used with two or more testsites comprising periodic gratings and possibly also solid(un-patterned) stacks, such that the test sites have at least one commonparameter. Such test sites can be measured to obtain diffractionproperties, and simultaneous optimization can be utilized. In thisconnection, the expression “simultaneous optimization” refers to aprocedure of obtaining a fit (a desired degree of fitting) of themeasured data and the theoretical sub-models for all (or the desirednumber) of the measured sites. Such a fitting may be realized by a totalfit criterion such as a merit figure calculated as the sum of meritfunctions. Typically, several periodic patterns are used in parallelinterpretation (simultaneous measurement).

Reference is made to FIGS. 2A-2F illustrating schematically multiplesites (six such sites respectively) of the same wafer. Generally, thesites constitute different stacks, namely stacks different in one ormore geometrical parameters (pattern parameters and/or layer thickness)and/or material-associated optical parameters, where such differentstacks may be associated with different locations on the article or mayresult from different processing steps of the same location on thearticle. Thus, this wafer includes a non-patterned site (FIG. 2A) andseveral patterned sites (FIGS. 2B-2F) having various geometricalparameters such as pitch and duty cycle values. In the sites of FIGS.2B-2D, only the uppermost layer is patterned and is in the form ofspaced-apart regions of Material 1 on top of Material 2 layer. Thesesites can for example be used for post development measurements inlithography. The sites of FIGS. 2B-2D are different from one another inthe pattern parameters and have common material composition parameters.In the sites of FIGS. 2E-2F, Material 1 is removed and two upper layers,Material 2 and Material 3 layers, are differently patterned. Thus, sitesof FIGS. 2B-2F are also different from one another in the patternparameters and have common material composition parameters. These sitesmay for example be measured after etching. In another option thepatterns can be buried under additional layers deposited after thepatterning.

Preferably, measurements are performed on close to one another sites ina single wafer. In these cases, the assumption that the globalparameters, common for the measured test sites, possess similar values(e.g. common thickness of under layers or common stack materialsparameters) is highly valid. This method can be performed withoutrequiring specially designed wafers, or specially processed short loopwafers by utilizing suitable sites already made on an existing wafer.

In some cases, where different conditions exist on different locationsof the wafer, measurements of different locations (e.g. test sites) mayprovide for additional information. For instance, when using a focusexposure matrix, the additional measurement information, to be processedin parallel, is associated with different focus and exposure conditions.Yet another option is working with different modes of operation of thesame measurement tool such as different NA (numerical aperture)conditions.

Reference is made to FIG. 3 exemplifying an optional step of preliminarydesign of test sites. According to certain embodiments of the invention,different sites on the wafer are designed or selected to serve as a testsite for measurements and optimization stages as noted above. This is apreliminary stage for the main optimization stage. This preliminarystage utilizes the ability to create a number of simple test sites (step1.6) of patterned structures (periodic structures) and possibly alsosolid structure(s) on the wafer, via a mask design. In this stage, thedesirable geometrical properties of the test sites are carefullyanalyzed and chosen according to the sensitivity of the parametersinvolved (i.e. to be measured or optimized) to variations of saidgeometrical properties and according to the degree of correlationbetween the effect of various geometrical properties to the measurementof the parameters involved. It is desired that the variations betweenthe test sites would provide an independent (or loosely correlated)indication of the parameters involved to thereby enable estimation andoptimization of these parameters through the results of the measurementsof the test sites.

Thus, this stage enables a control of the test sites, to be measured, bycareful mask design or careful choice of sites existing on a mask withemphasis on the sensitivity of the parameters involved.

An appropriate theoretical model (global model) is provided (step 1.1)including prediction of the materials involved and their properties(e.g. optical properties, reflectance/transmittance) and an optionaldesign of periodic/patterned test sites (such as gratings), typicallywith several degrees of freedom (e.g. period and duty-cycle (line-spaceratio)) varying for different test sites. An example of a globaltheoretical model characterizing a blanket wafer may include a set ofequations determining the expected complex refraction index (n(w)+ik(w))of the wafer and the dependence of said refraction index on severalparameters characterizing the wafer such as the material composition ofthe stack and the thickness of the layers. It should be understood thatdifferent models are created for analysis of different properties asrequired in the specific optimization problem at hand. Several testsites are designed or chosen from an existing mask (step 1.2). Thedesign includes specification of the geometrical propertiescharacterizing the sites and generation of a site-model (i.e. localmodel or sub-model corresponding to each site), based on the globalmodel obtained at step 1.1, for each of the test sites. The primary goalof design of said test sites is that the measurements of said siteswould provide adequate sensitivity of the respective model to theoptimized parameters and minimal correlations of those parameters to theother parameters involved in the model. Thus, the sub models (sitemodels) provide prediction of the expected measurement results of thesites. These sub-models are typically based on the global modeldescribed above with the addition of each site's geometrical parameters.

The design of the site is then verified (step 1.3). This includessensitivity and correlation analysis based on the theoretical model. Asindicated above and will be further described, at this stage the testsites are analyzed for their sensitivity to variations of certainparameters of interest. To this end, sensitivity is defined as a ratiobetween the expected effects in the measured data (e.g. the change inthe spectral transmittance/reflectance) that is caused due to avariation of the parameter under test (a change of fraction of the rangeof said parameter). This measure for sensitivity takes into accountnoise effects that may also affect the measured data, thus a sensitivityanalysis can be evaluated relative to typical deviation of the measuredquantity caused by noise. It is required that at least one of the testsites shows adequate sensitivity to a variation of a parameter ofinterest. A correlation analysis, also performed in this step, measuresthe degree of correlation between the sensitivities (as defined above)of the different test sites to variation of certain variables/parametersof interest. This is made to ensure “orthogonality” of these parameterswith respect to the chosen test sites and to enable deduction of each ofthe parameters of interest based on measurement results of the testsites chosen, and to ensure the final precision and stability inextraction of each parameter. This correlation analysis will be morespecifically described further below.

The sensitivity and correlation values of each of the parameters aretested against a threshold of the required final precision (step 1.4).When unsatisfactory precision values are obtained, the process startsagain, from step 1.5, providing corrections to the design of the testsites.

The verification step 1.3 ensures that the sensitive parameters do notsuffer from cross talk that can prevent the separate optimization. Suchanalysis allows for designing the best solution using predeterminedstructures. The use of such an initial analysis might be important evenin the case where mask structures are not or cannot be designed. In thiscase, a high level of confidence might be needed prior to starting ofthe fit and thus an understanding of the level of errors in parameteroptimization is critical.

When an appropriate design of the test sites is achieved and the errorvalues obtained in step 1.4 are below the predetermined threshold orotherwise minimal, the set of test sites and corresponding local (site)models or sub-models are obtained (step 1.6).

Turning now to FIGS. 5A-5C there is shown an example of the sensitivityanalysis. FIG. 5C shows a typical photoresist grating, e.g. photoresistline on top of BARC (Bottom Antireflective Coat) on Silicon substrate.FIGS. 5A and 5B show tables presenting sensitivity analysis of two testsites comprising photoresist gratings (of FIG. 5C) fabricated withdifferent geometrical properties. FIG. 5A shows sensitivity analysis ofa first test site with line width (i.e. CD) of 45 nm and duty cycle(i.e. line to space ratio) of 1:1 and FIG. 5B shows sensitivity analysisof a second test site with the same line width but with a duty cycle of1:5. In the tables shown in these figures several materialcharacteristics (parameters) and geometrical parameters characterizingthe test sites are arranged in decreasing order of sensitivity. As canbe seen, some material parameters are sensitive at the same level asgeometrical parameters.

However the sensitivity values depend on geometrical (grating)parameters such as the duty-cycle or period. This dependence isexemplified in the comparison between the tables of FIGS. 5A and 5B thatare calculated for different duty cycles. As can be seen from thetables, in this example both the sensitivity values assigned to eachparameter and the order of sensitivity of different parameters dependson the duty cycle. This also supplies a first indication that each oneof the parameters that are used in the optimization process is sensitiveenough with respect to the noise level and to the changed parameters.

A typical correlation analysis is illustrated in FIGS. 6A and 6B showingtwo cases of a wafer comprising a stack of two layers (marked L1 and L2in both figures) and the corresponding variance and correlation tables(e.g. using covariance matrixes) illustrating the correlation/couplingbetween the parameters and the predicted variance of measurement of theparameters involved (in units of one sigma standard deviation). Thewafer of FIG. 6A comprises a solid two-layer stack and the wafer of FIG.6B comprises a simple equal line space grating.

In FIG. 6A, two structural parameters, the thicknesses of the first andsecond layers (marked L1.Thickness and L2.Thickness), characterize thesolid wafer stack. A table of correlation analysis (shown in FIG. 6A,typical analysis from covariance matrix) characterizing the sensitivityof the measurements to variations in these parameters shows that thecorrelation between these parameters is strong (i.e. very close tounity, 0.9995) hence the conclusion that any one specific parametercannot be accurately predicted through the results of measurements ofthis structure. As shown, for a given confidence level, a measurementerror (sigma value) for thicknesses of layers L2 and L2 are respectively9.75 and 9.64, which are relatively high due to the high correlationbetween the parameters.

FIG. 6B presents a method used according to the present invention toovercome the limitation presented by the high correlation of theseparameters. A change of the structure characteristics, such as periodand duty-cycle, is performed to a level that will enable distinguishingbetween parameters due to a reduction of the correlation between theseparameters resulting from the changed/added geometrical features. Asshown in FIG. 6B a grating structure is presented, patterned on the toplayer (L1) and additional parameters are added (the line width marked asL1.CD and the wall angle marked as L1.A) corresponding to the gratinggeometry (the parameter L1.Thickness in FIG. 6A is substituted with L1.Hthe line height) in FIG. 6B. As evident from the correlation table shownin FIG. 6B the sensitivity correlation between the layers thicknessesthat was high (0.9995) in the case of FIG. 6A is now reduceddramatically, in the case of FIG. 6B, to a value of (0.0198) whichenables prediction of these parameters through the results ofmeasurements of this structure. Comparing data in FIGS. 6A and 6B, it isevident that the lower degree of correlation reduces the measurementerror for the layers' thicknesses.

It should be noted that the basic engine of such a correlation analysiscan be the known variance-covariance method, but it can also be anyother mathematical measure for correlations.

Hence, FIGS. 6A and 6B show a simple typical example in which use of aproperly designed grating structure can enable differentiation inmeasuring two thickness parameters which are otherwise inseparable dueto correlation between the parameters in a solid stack.

After implementation of the designed test sites on a mask (oralternatively choosing sites already existing on a mask or a wafer) andmanufacturing of wafers, reflectivity measurements are performed. Theinformation collected through the measurements of these sites isprocessed according to the process flow illustrated in FIG. 4 andfurther discussed below. Such information may comprise for example thespectral response values of reflectance at different sites or withdifferent measurements parameters such as irradiation or measurementangles.

FIG. 4 illustrates, in a way of flow diagram, an optimization stageprocess according to some embodiments of the present invention. Theoperation of this stage is based on utilizing and measuring existingsites which are present on a wafer. As indicated above the preliminarytest sites design/analysis stage can be utilized to choose betweenavailable structures on the wafer/mask or a design of customized sitesin order to obtain a set of measurements which can enable the requiredparameter separation.

Initially, in step (2.1), a wafer (typically one wafer on which therequired test sites are made) and the location of the chosen test sitesis supplied. A theoretical model of the wafer and a series of sub-models(local) corresponding to the test sites are obtained. Basically thesemodels are either given as an input from the optional preliminary designstage (described above with connection to FIG. 3) or they are created atthis stage.

A series of measurements (typically reflectance and transmittancemeasurements) for characterizing the optical properties of the testsites are carried out and the information comprising the measurementsresults is obtained in step (2.2).

A verification (step 2.3) is performed to ensure that the assumptions ofthe theoretical model are in agreement with the measurement results andthat there is a degree of similarity between the results predicted bythe model and the measurements. In case that the differences between thepredictions of the theoretical model and the measurements are too large,the local parameters of the sites are adjusted and optimized (steps 2.4,2.5). After the predictions of theoretical models (and the sub-models)are in good agreement with the measurements results, parameterverification (step 2.3) can be performed. Generally, these steps areoptional and are aimed at verifying that the sub-models accuratelydescribe the wafer and the test sites located thereon. This may beachieved by analyzing the deviations (that might occur in production) ofthe global and local parameters from the design and altering thetheoretical model accordingly. To this end, the term global/commonparameters refer to the structural and material parameters (e.g.differences in the layer widths) common to at least some of the testsites of the wafer. The term local parameters refers to the geometricalparameters different in the measured test sites.

Steps 2.4-2.7 of the flow diagram present an optional preliminaryoptimization process aimed at the adjustment and optimization of thelocal parameters (not common) and optionally also global parameters ofeach of the sub-models and corresponding test sites. Any inverse methodtechnique such as library search or injection (as described e.g. in U.S.Pat. No. 6,657,736 or US 2004/0042017 assigned to the assignee of thepresent application) are options to be used here as well.

Thus, the parameter verification process is carried out by two steps:geometry variation and material properties variation. In a geometryverification (step 2.4), careful analysis is performed for propergeometry description of the sites. A simple approach is the use oftrapezoidal shapes, and in increasing the number of the trapezoids inorder to better cover the grating-shapes involved. It is preferable touse a “process oriented approach” in which basic knowledge anddescription of the process influence on the geometry parameters, allowsbetter efficiency in the number of parameters and degrees of freedomrequired to define the structure.

The materials properties variation consists of materials physicalmodeling (step 2.5), implementing optimal mathematical modelingaccording to solid state physics materials knowledge, such as energy-gapand density of states.

During processing steps 2.4 and 2.5, the theoretical model and thesub-models are altered accordingly to reflect more accurately the actualparameters of the wafer and test sites as verified in these steps.Preferably, parameters are altered in each of the sub-models to providebetter level of fit of each of the sub-models with the measurements ofthe corresponding test site and without impairing the fit level of othersites (i.e. by not changing global parameters common to these sites).After preparing models for a proper description of each of the testsites, a best fit of measured spectrum to the calculated spectrum basedon the model should be achieved (step 2.6). In this case a specificpass/fail level of fit is defined for a merit function condition (step2.7). This also verifies that sufficient degrees of freedom areimplemented. After passing the single structure verifications, amulti-parameter global fit search is optionally performed on each of thestructures to get the best possible suggestions for the starting pointof the optimization of the global parameters (step 2.6). This minimizesany ambiguity. By performing it on several sites/stacks and using thecommonality of results between all sites, the correct global minimum andbest starting point is found.

Optionally, a measure (e.g. merit function) of the fit of each of thesites to the corresponding sub-model is tested against a predeterminedthreshold (step 2.7). When the fit level is insufficient and the globalfit fails, the process might commence again from step 2.3 until adesired fit level is achieved.

When an adequate fit level is achieved, an optimal starting point models(global and accordingly site-related sub-models) are obtained with agood agreement of prediction to the measurement results. This ensuresthat the models do not fall within a local minima of the fit functionand thus that the subsequent optimization processes would provide betteraccuracy.

In the next stage, the optimization process utilizes the starting pointmodels to further optimize the global model by using regression fitmethods and fitting the model with the measured results. Theoptimization/fitting method (e.g. regression fit) is performed byfitting a group of parameters at a turn in an order calculated accordingto the measured property sensitivity and according to the independencyof the effect of the parameter-associated property on variations ofother parameters. This ensures that the first parameters to be optimizedwithin the model provide the highest contribution to the fittingfunction but are less affected by subsequent optimizations of otherparameters.

Sensitivity and correlation analysis are then performed (step 2.8) basedon the new starting point models in a similar way as described in step1.3 of the preliminary design stage and exemplified in FIG. 3.

In the subsequent steps a regression is performed in which theparameters are repeatedly perturbed and the model is repeatedlyevaluated to minimize the differences between the modeled results andresults that have been empirically obtained. In order to perform theregression algorithm efficiently, sensitivity and correlation analysisfollow, at step 2.9, by ranking the estimated contribution of thedifferent parameters. The ranking signifies the importance andsignificance of the parameter. Parameters with higher ranking areincluded in the first steps of the regression, while others are includedin the subsequent steps to thereby enable first optimizing parameterswith higher influence on the fitting but with lower sensitivity(correlation) to subsequent variations of other parameters in the model.

The ranking of the parameters and the order of regression is chosen toensure sufficiently narrow confidence intervals in the model parametersand parameter correlation tables and enables to estimate the matchbetween theoretical data (calculated from the model) and the measureddata (step 2.10). In some cases, the other measured data may be used toensure higher level of confidence. For example, incorporating and/oromitting measured data of various wavelengths from the regression fitprocedure, etc. Then, an increasing number of parameters is successivelyincorporated in the optimization (steps 2.12 and 2.13), which is doneeither manually or by an automatic algorithm. The definition of thesteps for including additional parameters is based on the level ofachieved fit versus the calculated sensitivity.

To this end, the level of achieved fit is measured, e.g. by the totalmerit function. In this embodiment, the sum of squared differencesbetween the measured data and the model predictions are calculated foreach of the test sites to provide a fitting value (e.g. merit figure)for each of the sites. The sum of the sites fitting is calculated as atotal merit function. When the level of fit reaches some stoppingcriterion (step 2.12), the model and associated parameters are assumedto accurately reflect the measured data. One such stopping criterion isthat the fit level (e.g. the total sum of the merit function of the testsites) reaches some predetermined level. Another criterion is reachedwhen the reduction of the fit level becomes sufficiently small.

Other adaptive options can be implemented during the fitting process forbetter calculation time efficiency including varying the number ofwavelengths, using a progressively increasing number of diffractionmodes and increasing the density of angles describing the illuminationconditions (step 2.13).

A correlation analysis (step 2.14) is used to identify which sites arepreferable as starting points and which ones minimize the crosscorrelation in such a way that the confidence level achieved for eachparameter is improved. This analysis is performed at the start, eachtime an additional parameter is added and prior to including anadditional stack/site in the optimization process. With the progress ofthe optimization, more and more parameters with lower sensitivity areincluded and a better fit is achieved.

At some point, if the intermediate steps exhibit increasing deviationsfrom the starting point, based on excessive parameter variation and highlevel of merit function convergence, the stacks geometry and materialcomposition can be remodeled and the process restarted.

The optimization step of this algorithm (2.11) can utilize InverseRegression techniques or other similar techniques such as iterativesearch for the least square minima e.g. Simplex or Levenberg-Marquardtalgorithms. If calculation time is not a limitation, and the methodcannot overcome local minima, Global search algorithm can be applied(e.g. Simulated annealing or Branch and Bound methods).

It should be noted that as the method described above with reference toFIG. 4 can be utilized as a baseline for total automation of thescatterometry modeling. The basic idea here is to have link for the maskdata of the different sites and use some simple default assumptions forthe verification steps 2.4 and 2.5. The following self correction loopsare thus allowed: a starting point enhancement loop (steps 2.3, 2.6,2.7, 2.4/2.5) and an improvement loop for the global parameters (steps2.11, 2.12, 2.13). This way, existing mask information and basicmodeling assumptions will be refined at first for the local sites andsubsequently for global (common for these sites) parameters. The manualinvolvement in this case can be minimized and full automation cancorrect the initial assumptions.

The method can be applied to scatterometry overlay targets wheredifferent gratings exist on the wafer and may serve for the startingpoint for step 2.1. This relates to target design, moreover, the highaccuracy of material models coupled with common lateral shifts in thetargets can enable simultaneous analysis of overlay shifts duringin-line monitoring. This is due to the natural case of different periodsin scatterometry overlay targets (as compared to CD scatterometry). Inthis case, it is obvious that these targets are different structuresthat can be used for optimization. Another mode of work usingscatterometry overlay targets is the use of different deliberate shiftsin such targets to enable measurement of overlay. In this last case,each difference is in design and thus provides additional opticalinformation that can be used by the method as different structure.

Turning now to FIGS. 7A and 7B, a typical spectra fitting for threetypical sites in shift step is known and lithography is exemplified. Thesame numbers are used to identify the sites in both figures. A firstsite 71 is Solid Photoresist layer on Barc layer on Silicon substrate, asecond site 72 is Solid Barc Layer on Silicon substrate, and the thirdsite 73 is grating of Photoresist on Barc layer on Silicon substrate.FIG. 7A shows a typical situation during the optimization process. Theparameters of the optimization are selected according to theirsensitivity values (screen region 75). A diagram 76 for the measureddata curves versus the theoretical prediction curves are shown for eachof the sites analyzed and the fitting value 77, i.e. the merit functionof each of the site, is shown. The total merit function, i.e. the sum ofthe sites merit functions of FIG. 7A (not shown) is about 11.19 E−05.

FIG. 7B shows the spectra fitting state of the parameters afteroptimization process is completed. The total merit function 74 isreduced to 3.83 E−05.

The technique of the present invention can also be used for automaticrefinement of material parameters and other common parameters byapplying the SPWO method on measurements performed in serial productionthus enabling improved process control. FIG. 8 illustrates a flowdiagram of a possible implementation of a process control. In thisimplementation, two control process cycles A and B are carried outconcurrently during the production stage. Both cycles utilizemeasurements taken on standard production wafers. Cycle A is thestandard, fast process control cycle in which a previously definedexisting scatterometry model (step 120) is used for obtainingtheoretical scatterometry data and using this data for theinterpretation of measurements (step 130). To this end, measured data isprovided (e.g. during the production process)—step 140; this datatypically comes from a single site (or a small group of sites). Such aprocedure (cycle A) can be used for process control of geometricalparameters, e.g. CD. The second, longer process control cycle B utilizesmeasurements taken on a large number of test sites as input data fromthe same standard production wafers (step 160). The number of requiredsites to be measured is typically larger than the number of sites usedin cycle A. These measurements are then interpreted (step 150) accordingto the above-described SPWO methodology for the optimization of global(common) parameters such as material parameters and/or geometricalparameters. Cycles A and B processing procedures can be implemented bydifferent software modules of the same or different processor utilities,the cycle B being typically a separated, longer control loop procedure.The results of cycle B can be continuously fed into and refine thescatterometry model in order to follow up on changes in globalparameters, e.g. material properties. Alternatively, these results canbe used for flagging process problems. In case that library is used forthe interpretation of cycle A, the detection of significant deviation ofglobal parameters in cycle B may automatically trigger the rebuilding ofthe library. Another option is to implement cycle B to collect data fromcycle A using the same site(s) and to utilize the natural variability ofthe data typically occurring due to changes in some of the parameters asa function of time or coordinate across the wafer. Further option forimplementing the SWPO method would be instead of measuring amultiplicity of test sites with different geometrical parameters, tocreate the required variability in the measured data using variableprocess conditions across the wafer, e.g. focus exposure matrix.

The SPWO method of the present invention can be used in a specific caseas High-K metal gate (HKMG) application. In this case, the analysis ofmaterials that are under the poly layers cannot be characterized verywell. The reason for a characterization difficulty is that thePolySilicon layer becomes opaque in the UV. The analysis in thetraditional additive stack is limited because thermal conditions andcrystallinity of PolySilicon influence the Metal under the Poly and Polycrystal growth as well. Since the final process steps that include thethermal cycles and amorphization are normally done after the lithographysteps, the optical characterization is not possible on blankets orsolids. Moreover, the opaque nature of PolySilicon prevents any materialcharacterization in the UV region using the additive stack option.

What is claimed is:
 1. A method for characterizing properties of anarticle having a multi-layer structure comprising a multiplicity ofsites comprising different periodic patterns, the method comprising:providing a theoretical model of prediction indicative of opticalproperties of different stacks defined by geometrical and materialparameters of corresponding sites, said sites being common in at leastone of geometrical parameter and material parameter; selecting one ormore parameters of the article to be used for optimizing saidtheoretical model, the selecting comprising analyzing sensitivity ofsaid theoretical model to variation of each of the selected parameters,analyzing correlation between said selected parameters, and uponidentifying that the correlation does not satisfy a predeterminedcondition, changing at least one of the geometrical parameters of one ofthe stacks; performing optical measurements on at least two differentstacks of the article and generating optical measured data indicative ofthe geometrical parameters and material composition parameters for eachof the measured stacks; processing the optical measured data, saidprocessing comprising simultaneously fitting said optical measured datafor the multiple measured stacks with said theoretical model andextracting said at least one common parameter, thereby enabling tocharacterize the properties of the multi-layer structure within thesingle article; wherein any of said providing, performing, andprocessing are implemented in a machine.
 2. The method of claim 1,wherein said at least two different stacks include stacks associatedwith different locations, respectively, on the article.
 3. The method ofclaim 1, wherein said at least two different stacks include stacksassociated with the same location on the article and corresponding todifferent process steps applied to the article.
 4. The method of claim1, wherein said at least two different stacks include at least onepatterned site.
 5. The method of claim 1, wherein the geometricalparameters include layer thickness parameters.
 6. The method of claim 1,wherein the geometrical parameters include pattern parameters.
 7. Themethod of claim 6, wherein the pattern parameters include at least oneof the following: pitch, critical dimension, feature shape, featureheight, duty cycle.
 8. The method of claim 7, wherein the feature shapecomprise at least one of wall angle, wall shape, and rounding of thepattern feature.
 9. The method of claim 6, wherein the different stacksinclude periodic patterns with one or more different pattern parameters.10. The method of claim 1, wherein said optical measurements comprisespectral reflectometry and/or ellipsometry based measurements.
 11. Themethod of claim 1, wherein said different stacks are located within aproduct region of the article.
 12. The method of claim 1, wherein saidoptical measurements are performed in several steps of a process ofmanufacturing said article.
 13. The method of claim 1, wherein theselection of the stacks for measurements and/or the parameters isperformed on existing sites present on a mask.
 14. The method of claim1, wherein said correlation analysis comprises covariance analysistechnique.
 15. The method of claim 1, wherein said providing of thetheoretical model comprises selecting an appropriate theoretical modelenabling to match the measured data obtainable by said opticalmeasurements.
 16. The method of claim 1, wherein said providing of thetheoretical model comprises modeling the pattern using trapezoidalshapes.
 17. The method of claim 1, wherein the simultaneous fitting ofsaid optical measured data is a multi-parameter fitting procedure. 18.The method of claim 1, wherein said processing comprises fitting theoptical measured data for at least one separate stack from the multiplemeasured stacks with a corresponding theoretical model, and extractingat least one parameter of said at least one separate stack; andperforming said simultaneously fitting using said at least one parameterof the separate stack.
 19. The method of claim 1, comprising optimizingthe theoretical model, said optimizing comprising ranking the commonparameters in accordance with their estimated contribution to a degreeof fit between the theoretical model and the optical measured data. 20.The method of claim 19, wherein the estimated contribution comprises themodel sensitivity to the parameter variation and/or correlation betweenthe common parameters.